1. Field of the Invention
This invention relates generally to neural networks and, in particular, to methods and apparatus for testing the performance, and specifying the characteristics of, neural networks.
2. Description of the Prior Art
A large amount of literature exists in the field of artificial neural networks, or "neural nets". As one example, reference is made to Volumes 1 and 2 of "Parallel Distributed Processing-Explorations in the Microstructure of Cognition" by David E. Rumelhart, James E. McClelland and the PDP Research Group, The MIT Press, Cambridge, Mass. (1986). Reference is also made to U.S. Pat. No. 4,897,811, "N-Dimensional Coulomb Neural Network Which Provides For Cumulative Learning of Internal Representations" issued Jan. 30, 1990 to C. L. Scofield. This patent references a number of publications that describe various learning algorithms for multi-layer neural networks. Of particular interest herein are techniques for testing and/or specifying neural networks. A conventional technique, or metric, for testing a neural network sequentially presents the neural network with all examples of specific network accept and reject criteria, and then records the response of the neural network to each of the these inputs. However, the use of this technique may present a problem with large data sets, in that a considerable amount of time is required to present the entire data set to the network under test. Furthermore, as neural networks are embodied within trained hardware modules, the requirement for a sensitive metric becomes more important so as to adequately test the networks.
Neural network specifications for design applications consist primarily of connection weight values, architecture organization, and models of processing element(s). This is generally satisfactory for computer emulations. However, to accurately translate the specification into a hardware model, an additional requirement of design verification is presented.
As such, a problem arises when specialized neural network circuits are to be built and mass produced. The problem relates to both the specification of, and to the efficient testing, of the neural network.
Thus, an object of this invention is to provide an efficient metric for both testing and specifying a neural network.